Summary of Atlas3d: Physically Constrained Self-supporting Text-to-3d For Simulation and Fabrication, by Yunuo Chen et al.
Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication
by Yunuo Chen, Tianyi Xie, Zeshun Zong, Xuan Li, Feng Gao, Yin Yang, Ying Nian Wu, Chenfanfu Jiang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Atlas3D, an automatic method that enhances text-to-3D tools to generate self-supporting 3D models that adhere to physical laws. The existing methods focus on visual realism but neglect physical constraints, leading to instability issues in downstream tasks. Atlas3D combines a differentiable simulation-based loss function with physically inspired regularization, serving as either a refinement or post-processing module for existing frameworks. This approach is validated through extensive generation tasks and real-world environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Atlas3D makes it easier to create 3D models that can stand on their own and interact with the environment in games, robotics, and other applications. It’s like having a magic tool that makes sure your virtual objects won’t fall over! The paper also talks about how most current methods focus too much on making things look good but forget about physical rules. That’s not helpful if you want to use those 3D models in real-life situations. |
Keywords
» Artificial intelligence » Loss function » Regularization
